Method and apparatus for processing medical data

ABSTRACT

A computer implemented method for processing a plurality of medical indication conditions on a computer that includes a processor communicatively coupled to a memory includes obtaining a plurality of predetermined indication conditions which relate to a plurality of parameters, and forming a plurality of conditional segments based on respective values of the plurality of parameters defined in the plurality of predetermined indication conditions. Each conditional segment of the plurality of conditional segments corresponds to a combination of different value ranges of respective parameters of the plurality of parameters. The plurality of predetermined indication conditions includes a measurement item, a reference standard, a time period, a measurement condition, and a measurement manner.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a Continuation Application of U.S. patentapplication Ser. No. 14/186,079, filed on Feb. 21, 2014, which is basedon Chinese Patent Application No. 201310064224.4 filed on Feb. 28, 2013,the contents of which is incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION

The present invention relates to the field of data processing. Morespecifically, the present invention relates to a method and an apparatusfor processing data in the medical field.

With the widespread move to medical electronization, Electronic HealthRecords (EHR) are used to record the relevant medical data of patients.Usually, an EHR includes basic information, main diseases, and all theclinical records of a patient. The clinical record records clinicalconditions of a patient at different times (i.e., each time a patientvisits a doctor, every day during a given time period, etc.), whichincludes diagnosis, examination, and lab test results (such asmeasurement results concerning various physiological states). Theexisting EHR typically uses the XML language to record patient data inthe form of a Clinical Document Architecture (CDA).

On the other hand, authorities have issued clinical guidelines directedto different diseases with a lot of clinical practice and clinicalevidence to help a doctor to fully understand the conditions of apatient. Generally, a clinical guideline includes a plurality ofindications as well as judgment conditions of the indications. Forinstance, a clinical guideline concerning diabetes can contain anindication 1 of a controlled blood glucose and an indication 2 of apersistently high blood glucose. The condition of the indication 1 isthat 80% of blood glucose value in the latest one month satisfiesfasting blood glucose<7.5 mmol/L or 2 h blood glucose<10 mmol/L. Thecondition of the indication 2 is that 80% of blood glucose value inlatest three months satisfies fasting blood glucose>=9 mmol/L or 2 hblood glucose>=13 mmol/L. Determination of a single indication cannot bedirectly used for performing diagnosis and treatment of a disease, but acombination of a plurality of indications can help a doctor to fullyacquire comprehensive information of a patient. Since judgment andmatching of indications can be based on measurement results of apatient, therefore, it is desired to acquire patient data from an EHRand process patient data to match it with indication conditions in aclinical guideline and to provide more comprehensive patientinformation.

FIG. 1 schematically shows the manner of processing patient data in theprior art. As shown in FIG. 1, when matching and analysis are requiredfor a plurality of indication conditions 1-n in a clinical guideline, adata acquisition step 101, a data conversion step 102, and a conditionmatching step 103 are executed one by one as to each indicationcondition. Specifically, as to a certain indication condition i, at thedata acquisition step 101, patient data required by the indicationcondition i is acquired. For instance, as to indication condition of theabove indication 1, it is required to obtain fasting blood glucose dataand 2 h blood glucose data of a patient in the latest one month. Then,at the data conversion step 102, the obtained data is converted into acertain needed form. As mentioned above, the existing EHR uses a XMLlanguage to record the patient data in form of CDA. However, this formis not convenient for direct data analysis and matching. Therefore, atstep 102, patient data is converted from the CDA form to a form ofVirtual Medical Record (VMR). The VMR form can be represented as a treestructure with the patient as a root node and respective attributes ofobserved results as leaf nodes. By going through the tree structure, atconditional matching step 103, the patient data can be matched with theindication condition i, which means to analyze whether the patient datameets the indication condition i. After matching the indicationcondition i, the next indication condition is analyzed according to thesame steps 101-103. Thus, matching condition of the patient data withthe respective indication conditions in the clinical guideline can beobtained by processing and analyzing the patient data.

However, the above manner of processing the patient data is not ideal inexecution efficiency. The non-ideal efficiency is partly caused byredundant data processing. For instance, in order to analyze theindication condition 1, it is required to obtain fasting blood glucosedata and 2 h blood glucose data of a patient in the latest one month. Inorder to analyze the indication condition 2, fasting blood glucose dataand 2 h blood glucose data of the patient in the latest three months isobtained. Though data required by the indication condition 2 covers thedata required by the indication condition 1, according to the method ofFIG. 1, when the indication condition 2 is analyzed, it still requiresretrieval again from an EHR of all the blood glucose data in the latestthree months. As a result, the blood glucose data in the recent onemonth is retrieved repeatedly when analyzing the indication condition 1and the indication condition 2. Further, at the data conversion step102, the data above is converted again. At condition matching step 103,the above data is traversed for many times again. Obviously, suchredundant processing reduces processing efficiency of the patient data.In practice, a clinical guideline for a certain disease usually containsmore than one hundred, or even hundreds, of indication conditions. Sincethere are many indication conditions to be analyzed, processing of thepatient data usually costs a lot of time and cannot be performed inreal-time. This makes a doctor unable to obtain comprehensiveinformation of a patient in efficient time.

Therefore, an improved solution is desired to improve processingefficiency of patient data.

SUMMARY OF THE INVENTION

In consideration of the above-mentioned problems existing in the priorart, the present invention improves processing efficiency of patientdata.

Accordingly, a first aspect of the present invention provides a methodfor processing indication conditions, including: obtaining a pluralityof predetermined indication conditions which relate to a plurality ofparameters; and forming a plurality of conditional segments based onrespective values of the plurality of parameters defined in theplurality of indication conditions, wherein the plurality of conditionalsegments respectively correspond to a plurality of combinations of valueranges of the plurality of parameters.

A second aspect of the present invention provides a method forprocessing patient data, including: obtaining distribution informationof the patient data in a plurality of conditional segments formed as toa plurality of indication conditions using the method according to thefirst aspect; and determining a matching relation of the patient datawith at least one indication condition of the plurality of indicationconditions based on the distribution information.

A third aspect of the present invention provides an apparatus forprocessing indication conditions, including: an indication conditionobtaining unit configured to obtain a plurality of predeterminedindication conditions which relate to a plurality of parameters; and aconditional segment forming unit configured to form a plurality ofconditional segments based on respective values of the plurality ofparameters defined in the plurality of indication conditions, whereinthe plurality of conditional segments respectively correspond to aplurality of combinations of value ranges of the plurality ofparameters.

A fourth aspect of the present invention provides an apparatus forprocessing patient data, including: a distribution obtaining unitconfigured to obtain distribution information of the patient data in aplurality of conditional segments formed as to a plurality of indicationconditions using the apparatus according to the third aspect; and amatching determining unit configured to determine a matching relation ofthe patient data with at least one indication condition of the pluralityof indication conditions based on the distribution information.

Using the methods and apparatuses of the embodiments of the presentinvention, matching related patient data with a plurality of indicationconditions can be directly determined based on distribution informationof the patient data in the respective formed conditional segments. Asthe number of times redundant data is obtained and data conversion isreduced, the methods and apparatuses of the embodiments of the presentinvention improve processing efficiency of patient data.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some embodiments of the presentinvention in the accompanying drawings, the above and other objects,features, and advantages of the present invention are made moreapparent. The same reference generally refers to the same components inthe embodiments of the present invention.

FIG. 1 schematically shows the manner of processing patient data in theprior art.

FIG. 2 shows a block diagram of an exemplary computer system/serverwhich is applicable to implement the embodiments of the presentinvention.

FIG. 3 shows a method for processing indication conditions according toan embodiment of the present invention.

FIG. 4 shows sub-steps of a method for forming a plurality ofconditional segments according to an embodiment of the presentinvention.

FIGS. 5A-5B show a process diagram of forming conditional segmentsaccording to an embodiment of the present invention. More specifically:

FIG. 5A shows conditional segments of two levels; and

FIG. 5B shows conditional segments of three levels.

FIG. 6 shows a plurality of conditional segments formed according to anembodiment of the present invention.

FIG. 7 shows a plurality of conditional segments formed according toanother embodiment of the present invention.

FIG. 8 shows a method for processing patient data according to anembodiment of the present invention.

FIG. 9A shows a schematic block diagram of an apparatus for processingindication conditions according to an embodiment of the presentinvention.

FIG. 9B shows a schematic block diagram of an apparatus for processingpatient data according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Some preferable embodiments are described in more detail with referenceto the accompanying drawings, in which the preferable embodiments of thepresent invention have been illustrated. However, the present inventioncan be implemented in various manners and, thus, should not be construedto be limited to the embodiments disclosed herein. On the contrary,those embodiments are provided for the thorough and completeunderstanding of the present invention and to completely convey thescope of the present invention to those skilled in the art.

As can be appreciated by one skilled in the art, aspects of the presentinvention can be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention can take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that can allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present invention can take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) can beutilized. The computer readable medium can be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium can be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium can include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium can be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium can include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal can takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium can be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium can be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention can be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code can execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer can be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection can be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams and combinations of blocks in theflowchart illustrations and/or block diagrams can be implemented bycomputer program instructions. These computer program instructions canbe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions can also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions can also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Refer now to FIG. 2, in which an exemplary computer system/server 12which is applicable to implement the embodiments of the presentinvention is shown. Computer system/server 12 is only illustrative andis not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the present invention described herein.

As shown in FIG. 2, computer system/server 12 is shown in the form of ageneral-purpose computing device. The components of computersystem/server 12 can include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media can be any available media that isaccessible by computer system/server 12 and it includes both volatileand non-volatile media and removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 can further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”) and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As is further depicted and described below, memory 28can include at least one program product having a set (e.g., at leastone) of program modules that are configured to carry out the functionsof embodiments of the present invention.

Program/utility 40, having a set (at least one) of program modules 42,can be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, can include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 can also communicate with one or more externaldevices 14, such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components can be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Below, executing manners of the present invention are described withreference to the Figures. According to embodiments of the presentinvention, in comprehensive consideration of a plurality of indicationconditions, a plurality of conditional segments are formed based onrespective parameters involved in the indication conditions. Thendistribution of patient data in respective conditional segments isanalyzed and a matching relation of the patient data with the respectiveindication conditions is determined based on such distributioninformation. Thus, redundant data obtaining and data convertingoperations are lessened or avoided. Respective embodiments of thepresent invention for implementing the idea above are described indetail below.

Reference is now made to FIG. 3, which illustrates a method forprocessing indication conditions according to an embodiment of thepresent invention. As shown in FIG. 3, a method for processing medicalindication conditions according to an embodiment of the presentinvention includes: step 30, obtaining a plurality of predeterminedindication conditions which relate to a plurality of parameters; andstep 32, forming a plurality of conditional segments based on respectivevalues of the plurality of parameters defined in the plurality ofindication conditions, wherein the plurality of conditional segmentsrespectively correspond to a plurality of combinations of value rangesof the plurality of parameters. Below, executions of the respectivesteps above are described in combination with concrete examples.

First, at step 30, a plurality of predetermined indication conditionsare obtained. In an embodiment of the present invention, the pluralityof predetermined indication conditions are indication conditionscontained in a clinical guideline. As the clinical guideline is alreadyrecorded and stored in computerized manner according to prior art,therefore, at step 30, several indication conditions of interest can beread directly from the computerized clinical guideline. In a concreteexample, the plurality of indication conditions refer to all indicationconditions contained in a clinical guideline directed to a certaindisease. It can be appreciated, in other embodiments of the presentinvention, that predefined indication conditions can also be obtainedfrom other data sources. Generally, an indication condition relates to aplurality of parameters which typically include measurement item,reference standard, and time period. In some cases, the indicationconditions can further relate to other parameters, such as measurementconditions, measurement manners, etc.

Based on respective values of the plurality of parameters defined in theplurality of indication conditions mentioned above, at step 32, aplurality of conditional segments are formed, wherein the plurality ofconditional segments respectively correspond to a plurality ofcombinations of different value ranges of the plurality of parameters.Below, execution of the step 32 above is described in connection withexamples of three indication conditions.

Indication 1: Controlled Blood Glucose

Indication Condition 1: 80% of blood glucose values in the recent onemonth satisfies: fasting blood glucose<7.5 mmol/L or 2 h bloodglucose<10 mmol/L.

Indication 2: Blood Glucose Continues High

Indication Condition 2: 80% of blood glucose values in the recent threemonths satisfies: fasting blood glucose>=9 mmol/L or 2 h bloodglucose>=13 mmol/L.

Indication 3: Hypoglycemia

Indication Condition 3: the latest blood glucose<3.9 mmol/L.

As can be seen, all the parameters involved in the Indication Conditions1-3 above include measurement item, reference standard, and time period.In Indication Condition 1, the measurement item is valued as fastingblood glucose or 2 h blood glucose, the reference standard is valued asfasting blood glucose 7.5 mmol/L as well as 2 h blood glucose 10 mmol/L,and the time period is valued as the most recent one month. Similarly,values of parameters (measurement item, reference standard, and timeperiod) involved in Indication Conditions 2 and 3 can be obtained. Basedon the values, at step 32, a plurality of conditional segments areformed to define a plurality of possible states of the patient data.

FIG. 4 shows sub-steps of forming a plurality of conditional segmentsaccording to an embodiment of the present invention (i.e. sub-steps ofthe step 32 above). As shown in FIG. 4, first, at step 321, a firstparameter is selected from the plurality of parameters to form a firstlevel of conditional segments based on values of the first parameterdefined in the plurality of indication conditions. Then, at step 322,another parameter is selected from the rest of the parameters to form aplurality of range segments based on values of the other parameterdefined in the plurality of indication conditions. At step 323, theplurality of range segments are combined with conditional segments in ahigher level to form a new level of conditional segments. Then, at step324, it is judged whether the plurality of parameters have beenprocessed. If they have not, steps 322-324 can be executed repeatedlyuntil all the plurality of parameters are processed. In case that allthe plurality of parameters have been processed, at step 325,conditional segments of the newest level formed now is determined as theplurality of conditional segments required.

As to the Indication Conditions 1-3 above, involved parameters includemeasurement item, reference standard, and time period. In an example, atstep 321, the measurement item is selected as a first parameter.Furthermore, values of the first parameter in respective indicationconditions are obtained. As to the measurement item, values given byIndication Conditions 1-3 include fasting blood glucose and 2 h bloodglucose, wherein the blood glucose measurement in Indication Condition 3includes the fasting blood glucose and 2 h blood glucose. Therefore, afirst level of conditional segments is formed based on the two values.In other words, at the first level, 2 conditional segments (i.e. fastingblood glucose and 2 h blood glucose) are defined so as to divide patientdata into two parts which respectively meet the two conditionalsegments.

Then, at step 322, another parameter is selected from the rest of theparameters and values of the another parameter in respective indicationconditions are obtained to form a plurality of range segments. As themeasurement item has been selected as a first parameter, in an example,the reference standard is selected as another parameter. After analyzingthe Indication Conditions 1-3, respective values of the referencestandard can be easily extracted to obtain the following information:the Indication Condition 1 has the following defined values of thereference standard: 7.5 mmol/L and 10 mmol/L; values defined by theIndication Condition 2 include: 9 mmol/L and 13 mmol/L; and a valuedefined in the Indication Condition 3 includes 3.9 mmol/L. Based on suchinformation, respective values obtained from the extraction are orderedand adjacent values are combined as a segment so as to obtain aplurality of value ranges. For respective values of the referencestandard above, the following range segments can be formed: <3.9, [3.9,7.5), [7.5, 9), [9, 10), [10, 13), and >=13.

Then, at step 323, the plurality of range segments are combined withconditional segments in a higher level to form a new level ofconditional segments. In the current example, conditional segments inthe higher level are the first level conditional segments formed at step321. More specifically, two conditional segments defined by the fastingblood glucose and 2 h blood glucose. Thus, at step 323, the plurality ofrange segments formed by respective values of the reference standard areused to further divide the 2 conditional segments of the first level toform a new level of conditional segments. In an example, respectiverange segments formed are directly combined with respective conditionalsegments of a higher level. In this case, if there has formed “n”conditional segments in the higher level and “m” ranges at step 322,then n*m conditional segments are obtained in the new level. In anotherembodiment of the present invention, the association between parametersdefined in respective indication conditions is taken into considerationto combine the range segments formed at step 322 with conditionalsegments of higher level. Specifically, the value “<7.5” of thereference standard defined in Indication Condition 1 is only directed tothe fasting blood glucose and the value “<10 mmol/L” of the referencestandard is only directed to the 2 h blood glucose. Therefore, the rangesegment which relates to the value 7.5 mmol/L is only required to becombined with the conditional segment of the fasting blood glucose inthe first level and the range segment which relates to the value 10mmol/L is only required to be combined with the conditional segment ofthe 2 h blood glucose in the first level.

FIG. 5 shows a process diagram of forming conditional segments accordingto an embodiment of the present invention. In particular, FIG. 5A showsconditional segments of two levels, wherein the first level ofconditional segments is formed based on values of measurement item atstep 321 and the second level of conditional segments is formed based onvalues of the reference standard as mentioned before in steps 322 and323. Specifically, in FIG. 5A, the association between the measurementitem and reference standard defined in the indication conditions isconsidered and, therefore, in the second level only range segments <3.9,[3.9, 7.5), [7.5, 9), >=9 are combined with the conditional segment“fasting blood glucose” in the first level. Correspondingly, rangesegments in combination with the conditional segment “2 h blood glucose”in the first level are: <3.9, [3.9, 10), [10, 13), >=13. Therefore, atthe second level, 8 conditional segments are formed.

As mentioned above, it is necessary to execute steps 322-323 repeatedlyuntil all the plurality of parameters are processed. The conditionalsegments formed in FIG. 5A only take the two parameters of measurementitem and reference standard into consideration. Therefore, then, as tothe rest another parameter (i.e. time period) steps 322 and 323 areexecuted again.

Similarly, at step 322, values of the other parameter “time period” inrespective indication conditions are obtained to form a plurality ofrange segments. In Indication Conditions 1-3, the following values aredefined as to the time period: the recent one month, the recent threemonths, and the latest time. Thus, the following range segments can beformed: the latest time, within the recent one month, and from therecent one month to three months. The range segments can be expressedas: latest, <1 m, [1 m, 3 m). Then, at step 323, the plurality of rangesegments above are combined with conditional segments in higher levels(i.e. conditional segments in the first and second levels as shown inFIG. 5A to form a new level of conditional segments). As mentionedabove, by considering the association between the parameters defined inthe indication conditions, it can be determined that the range segmentof “the latest” is only specified in association with the referencestandard <3.9 in the Indication Condition 3 and, thus, this rangesegment is only required to be combined with conditional segment of“<3.9” among the higher level conditional segments. Therefore, bycombining the range segments formed by the time period with conditionalsegments of higher levels selectively, a third level of conditionalsegments is formed as shown in FIG. 5B. These conditional segments arenumber-labeled as conditional segments (1)-(9). In FIG. 5B, conditionalsegments of the branch of 2 h blood glucose in the third level isomitted for clarity and conciseness.

As to the Indication Conditions 1-3, by executing the steps 321-323above, a plurality of conditional segments, as shown in FIG. 5B, areformed in levels. At this time, the parameters (i.e. measurement item,reference standard, and time period) involved in the IndicationConditions 1-3 have all been processed. Therefore, the formed new levelconditional segments (i.e. the third level conditional segments) aretaken as required conditional segments. Here, each conditional segmentcorresponds to a combination of different value ranges of respectiveparameters. For instance, the conditional segment (1) corresponds to acombination that the measurement item is valued as fasting bloodglucose, the reference standard is valued as <3.9 mmol/L, and the timeperiod is valued as “the latest” at the same time. The conditionalsegment (4) corresponds to a combination that the measurement item isvalued as fasting blood glucose, the reference standard is valued as[3.9, 7.5), and the time period is valued as within one month.

In order to form the needed conditional segments, in the forming processas shown in FIG. 5, at the first level, measurement item is selected asa first parameter to form conditional segments of the first level basedon the values thereof. Then, at the second and the third levels, newlevels of conditional segments are formed based on values of thereference standard and time period, respectively. However, suchselection order is not the only choice. In an example, at step 321, thefirst level conditional segments are formed still based on values of themeasurement item. However, then, at the second level, conditionalsegments are formed based on values of the time period and then thethird level conditional segments are formed based on value of thereference standard. Thus, conditional segments as shown in FIG. 6 areobtained.

In another example, at step 321, the first level conditional segmentsare formed based on values of the time period. Then, the second levelconditional segments are formed based on the measurement item and thelast level conditional segments are formed based on values of thereference standard. Accordingly, the conditional segments as shown inFIG. 7 are obtained. It can be appreciated that other orders are alsopossible to form the respective conditional segments.

Formation of conditional segments are described in connection withexamples of concrete Indication Conditions 1-3 above. It can beappreciated, in other examples, that the parameters in the indicationconditions can have different values than those in the examples above.For instance, the measurement item can further be valued as blood lipid,blood pressure, etc. Correspondingly, the reference standard can also bedifferent. With respect to these different examples, steps 321-323 canbe executed similarly to obtain required conditional segments. Moregenerally, it is possible that other indication conditions can relate todifferent parameters or more parameters, such as measurement condition,measurement manner, etc. In these cases, besides executing steps 321-323above for different parameters similarly, it can be necessary to executesteps 322-323 for more times repeatedly to process more parameters toobtain conditional segments of more levels.

The conditional segments formed in the method above respectivelycorrespond to various combinations of value ranges of parameters definedin respective indication conditions. Such combinations can further beused for dividing patient data and, thus, matching the patient data withrespective indication conditions. In other words, the conditionalsegments formed directed to the indication conditions can be used forprocessing patient data. More specifically, for analyzing the matchingof the patient data with respective conditions.

Therefore, based on the conditional segments formed above, embodimentsof the present invention further provide a method for processing patientdata. FIG. 8 is a flowchart showing a method for processing patient dataaccording to an embodiment of the present invention. As shown in FIG. 8,at step 82, distribution information of the patient data in a pluralityof conditional segments formed directed to a plurality of indicationconditions using the method above is obtained. At step 84, a matchingrelation of the patient data with at least one indication condition ofthe plurality of indication conditions is determined based on thedistribution information. Below, concrete execution processes of thesteps above are described.

At step 82, distribution information of patient data in a plurality ofconditional segments is obtained and the plurality of conditionalsegments are formed according to the method in FIG. 3 directed to aplurality of indication conditions. In an embodiment of the presentinvention, counters are set for the respective formed conditionalsegments and, at step 82, numbers of patient data falling intocorresponding conditional segments are counted using counters ofrespective conditional segments. Specifically, at step 82, patient datais obtained from an EHR and a format conversion is performed on thepatient data when necessary. Based on this, respective patient data ismatched with respective formed conditional segments. Specifically,attribute values of the patient data (such as measurement time,measurement item, and measurement values) are compared to value rangesof parameters corresponding to respective conditional segments so as todetermine a conditional segment to which the patient data belongs. As toa specific conditional segment, once certain patient data is determinedas belonging to said conditional segment or matching with theconditional segment, the counter of the specific conditional segment isincremented by 1. Thus, counters of respective conditional segments canreflect the number of patient data falling into the correspondingconditional segments. Therefore, statistical results of respectiveconditional segments can be obtained by reading the counting of thecounters of respective conditional segments so as to obtain distributionof the patient data in respective conditional segments.

Specifically, as to some specific conditional segments, a register or amemory can be set to store more information. For instance, as to theconditional segment with time period valued as “the latest” in FIGS. 5to 7, a memory is set additionally for storing measurement time ofpatient data falling into the conditional segment. Each time when newdata enters into this conditional segment, measurement time of the newdata is compared to the stored measurement time. If the measurement timeof the new data is later than the stored measurement time, then themeasurement time of the new data replaces the formerly storedmeasurement time. If the measurement time of the new data is earlierthan the stored measurement time, then the formerly stored measurementtime is retained.

In an embodiment of the present invention, besides a plurality ofconditional segments formed according to the method of FIG. 3, an“other” conditional segment is set additionally to contain data whichdoes not belong to any one conditional segment formed according to themethod. Alternatively, in another embodiment of the present invention,data which does not belong to any one conditional segment above is notrecorded.

In another embodiment of the present invention, in order to avoidunnecessary data matching operations at step 82, step 80 (shown withdotted lines in FIG. 8) is executed before step 82, wherein the requiredpatient data is determined based on the indication conditions so as tonarrow down the range of patient data to be processed at step 82. Below,descriptions are made in connection with the examples of IndicationConditions 1-3, wherein the involved parameters include measurementitem, reference standard, and time period.

In another embodiment of the present invention, the required patientdata is obtained transversely based on values of specific parameters inthe plurality of indication conditions. Specifically, step 80 caninclude determining the required patient data based on a union of valueranges of at least one parameter in the plurality of parameters definedin the indication conditions.

In another embodiment of the present invention, at step 80, the requiredpatient data is determined based on a union of value ranges of theparameter “measurement item.” Specifically, the measurement item isrespectively valued as fasting blood glucose and 2 h blood glucose inIndication Conditions 1-3 and, thus, the union thereof is a collectionof fasting blood glucose and 2 h blood glucose. Therefore, at step 80,data concerning the fasting blood glucose and 2 h blood glucose in thepatient data is determined as the required patient data.

In another embodiment of the present invention, at step 80, the requiredpatient data is determined based on a union of value ranges of theparameter “time period.” Specifically, the time period is respectivelyvalued as recent month, recent 3 months, and latest time in IndicationConditions 1-3 and, thus, the union thereof is the recent 3 months.Therefore, at step 80, all data in recent 3 months is determined as therequired patient data.

In another embodiment of the present invention, two parameters are takeninto consideration to determine the required patient data. In this case,step 80 can include, determining a first data portion based on a unionof value ranges of a first parameter in the plurality of parametersdefined in the indication conditions; determining a second data portionbased on a union of value ranges of a second parameter in the pluralityof parameters defined in the indication conditions; and determining anintersection of the first data portion and the second data portion asthe required patient data.

In an example, measurement item is taken as the first parameter and timeperiod is taken as the second parameter. According to analysis in theprevious embodiments, data concerning the fasting blood glucose and 2 hblood glucose in the patient data can be determined as the first dataportion and all data in recent 3 months can be determined as the seconddata portion. Then, data concerning the fasting blood glucose and 2 hblood glucose in recent 3 months can be determined as the requiredpatient data by getting an intersection of the first data portion andthe second data portion.

It can be appreciated, in case that more parameters are considered todetermine the required patient data, based on the two data portionsaccording to two parameters, another data portion can be determinedaccording to a union of values of another parameter and then anintersection of the another data portion with the formerly obtained dataportion is taken so as to further narrow down range of the patient datato be analyzed.

In another embodiment of the present invention, the required patientdata can further be determined by considering respective indicationconditions one by one. Specifically, step 80 can include determiningdata portions required by respective indication conditions based ondefinitions of respective indication conditions; and obtaining a unionof data portions required by respective indication conditions as therequired patient data.

In an example, the Indication Conditions 1-3 are taken intoconsideration one by one to determine the required data portionsrespectively. For instance, according to definition of the IndicationCondition 1, it can be determined that the data portion required by theIndication Condition 1 includes data concerning the fasting bloodglucose in one month and data concerning the 2 h blood glucose in onemonth of a patient. Similarly, the data portions required by theIndication Condition 2 and Indication Condition 3 are determinedrespectively. Then, a union of the data portions for respectiveindication conditions obtained is taken to determine the requiredpatient data.

The processes of getting a union or intersection as to different valueranges and different data portions can be performed using variousmathematical analysis methods in the prior art, which will not beexplained further here.

Step 80 excludes data which cannot possibly fall into any conditionalsegment formed according to FIG. 3, reduces the amount of data to beanalyzed at step 82, improves the processing efficiency of step 82, and,thus, facilitates the process of obtaining distribution information ofthe patient data in respective conditional segments.

Based on distribution information of the patient data in respectiveconditional segments, at step 84, a matching relation of the patientdata with at least one indication condition of the plurality ofindication conditions is analyzed based on the distribution information.For this purpose, a specific indication condition to be analyzed isfirstly converted into operation as to data distribution of at least oneconditional segment and then a matching relation of the patient datawith the specific indication conditions is determined based on datadistribution of the at least one conditional segment above.

For instance, Indication Condition 1 includes the following condition:80% of blood glucose value in one month satisfies: fasting bloodglucose<7.5 mmol/L. The condition can be converted into the followingoperation: obtaining a ratio of the number of measurement dataconcerning the fasting blood glucose in one month whose measurementresult is <7.5 mmol/L to the total number of the data concerning thefasting blood glucose in one month and determining whether the ratio islower than 80% or not. In the case that conditional segments forIndication Conditions 1-3 are formed as shown in FIG. 5B, the ratioabove corresponds to the following expression:(C(2)+C(4))/(C(2)+C(4)+C(6)+C(8)), wherein C(i) indicates data countingdistributed in the conditional segment (i). Thus, the above ratio can becalculated directly by obtaining data distribution of conditionalsegments (2), (4), (6) and (8) (i.e. counting number of data fallingtherein) so as to determine whether the patient data matches theIndication Condition 1. The Indication Condition 2 can also be convertedinto similar operations. Indication Condition 3 can be converted intooperation of judging whether counting of the conditional segment (1) is0. As the conditional segments are formed based on respective indicationconditions, the indication conditions can be easily converted intooperations on data distribution results as to conditional segments. Itcan be appreciated, when matching analysis is performed as to respectiveindication conditions at step 84, it is only required to directly usethe data distribution of respective conditional segments obtained instep 82 without needing to read, convert, or analyze the patient dataagain.

Therefore, in general, in the embodiments of the present inventiondescribed above, a plurality of conditional segments can be formeddirected to a plurality of indication conditions. Then distributioninformation of the patient data in respective conditional segments canbe obtained. Thus, for any indication condition, a matching relation ofthe patient data therewith can be directly determined based on thedistribution information. Compared to the processing manner ofperforming data acquisition, conversion, and analysis as to eachindication condition in the prior art, the embodiments of the presentinvention avoid chances of efficiency reduction caused by multiple dataacquisitions, conversions, and analysis so as to realize more efficientprocessing and analysis of patient data.

Based on the same inventive concept, embodiments of the presentinvention further provide an apparatus for processing indicationconditions and an apparatus for processing patient data. FIG. 9A shows aschematic block diagram of the apparatus for processing indicationconditions according to an embodiment of the present invention. In FIG.9A, the apparatus is indicated as a whole as 91. As shown in the Figure,the apparatus 91 for processing indication conditions includes: anindication condition obtaining unit 910 configured to obtain a pluralityof predetermined indication conditions which relate to a plurality ofparameters; and a conditional segment forming unit 912 configured toform a plurality of conditional segments based on respective values ofthe plurality of parameters defined in the plurality of indicationconditions, wherein the plurality of conditional segments respectivelycorrespond to a plurality of combinations of value ranges of theplurality of parameters.

According to an embodiment of the present invention, the conditionalsegment forming unit 912 includes (not shown): a first level formingmodule configured to select a first parameter from the plurality ofparameters to form a first level of conditional segments based on valuesof the first parameter defined in the plurality of indicationconditions; a range segment forming module configured to select anotherparameter from the rest of the parameters to form a plurality of rangesegments based on values of the another parameter defined in theplurality of indication conditions; and a combining module configured tocombine the plurality of range segments with a higher level ofconditional segments to form a new level of conditional segments;wherein the range segment forming module and the combining module areexecuted repeatedly until all the plurality of parameters are processedand the new level of conditional segments formed then is determined asthe plurality of conditional segments.

According to an embodiment of the present invention, the combiningmodule above is configured to combine the plurality of range segmentswith a higher level of conditional segments by taking associationbetween a plurality of parameters defined in the plurality of indicationconditions into consideration.

According to an embodiment of the present invention, the indicationcondition obtaining unit 910 is configured to obtain the plurality ofindication conditions from a clinical guideline and the plurality ofparameters, including measurement item, reference standard, and timeperiod.

FIG. 9B shows a schematic block diagram of an apparatus for processingpatient data according to an embodiment of the present invention. InFIG. 9B, the apparatus is indicated as a whole as 92, including: adistribution obtaining unit 922 configured to obtain distributioninformation of the patient data in a plurality of conditional segmentsformed as to a plurality of indication conditions using the apparatusaccording to FIG. 9A; and a matching determining unit 924 configured todetermine a matching relation of the patient data with at least oneindication condition of the plurality of indication conditions based onthe distribution information.

According to an embodiment of the present invention, the distributionobtaining unit 922 is configured to: count numbers of patient datafalling into corresponding conditional segments using counters set forthe plurality of conditional segments; and obtain distribution of thepatient data in respective conditional segments by reading the countingof the counters of the plurality of conditional segments.

According to an embodiment of the present invention, the apparatus 92further includes a data determining unit 920 (shown with dotted lines)configured to determine required patient data based on a union of valueranges of at least one parameter in the plurality of parameters definedin the plurality of indication conditions and the distribution obtainingunit is configured to determine distribution of the required patientdata in the plurality of conditional segments.

According to an embodiment of the present invention, the datadetermining unit 920 is configured to determine a first data portionbased on a union of value ranges of a first parameter in the pluralityof parameters in the plurality of indication conditions; determine asecond data portion based on a union of value ranges of a secondparameter in the plurality of parameters in the plurality of indicationconditions; and determine an intersection of the first data portion andthe second data portion as the required patient data.

According to an embodiment of the present invention, the matchingdetermining unit 924 is configured to: convert the at least oneindication condition into an operation of data distribution of at leastone condition segment; and determine a matching relation of the patientdata with the at least one indication condition based on the datadistribution of the at least one conditional segment.

Concrete executing manners of the apparatus 91 for processing indicationconditions and apparatus 92 for processing patient data above can referto the description of the method in connection with the concreteexamples afore, which will not be detailed again.

The methods and apparatuses of the embodiments of the present inventionreduce or avoid chances of efficiency reduction caused by multiple dataacquisitions, conversions, and analysis so as to realize more efficientprocessing and analysis of patient data.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof code, which includes one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock can occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks can sometimes be executed in the reverseorder, depending upon the functionality involved. It is also noted thateach block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, and/or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer implemented method for processing aplurality of medical indication conditions on a computer that includes aprocessor communicatively coupled to a memory, the method comprising:obtaining a plurality of predetermined indication conditions whichrelate to a plurality of parameters; and forming a plurality ofconditional segments based on respective values of said plurality ofparameters defined in said plurality of predetermined indicationconditions, wherein each conditional segment of said plurality ofconditional segments corresponds to a combination of different valueranges of respective parameters of said plurality of parameters, andwherein said plurality of predetermined indication conditions comprise ameasurement item, a reference standard, a time period, a measurementcondition, and a measurement manner.
 2. The computer implemented methodaccording to claim 1, wherein the forming said plurality of conditionalsegments comprises: selecting a first parameter from said plurality ofparameters to form a first level of conditional segments based on valuesof said first parameter defined in said plurality of indicationconditions; and selecting an another parameter from the rest of saidplurality of parameters to form a plurality of range segments based onvalues of said another parameter defined in said plurality of indicationconditions.
 3. The computer implemented method according to claim 2,wherein the combining said plurality of range segments with said higherlevel of conditional segments comprises: combining said plurality ofrange segments with said higher level of conditional segments by takinginto consideration the association between said plurality of parametersdefined in said plurality of indication conditions.
 4. The computerimplemented method according to claim 2, wherein the forming saidplurality of conditional segments comprises: dividing conditionalsegments of a higher level of the conditional segments to form a newlevel of conditional segments.
 5. The computer implemented methodaccording to claim 4, wherein the forming said plurality of rangesegments and the forming said new level of conditional segments areexecuted repeatedly until all said plurality of parameters are processedand said new level of conditional segments formed then is determined assaid plurality of conditional segments.
 6. The computer implementedmethod according to claim 1, wherein the forming said plurality ofconditional segments comprises: dividing conditional segments of ahigher level of the conditional segments to form a new level ofconditional segments.
 7. A computer implemented method for processing aplurality of patient data, wherein the computer includes a processorcommunicatively coupled to a memory, the method comprising: obtaining aplurality of predetermined indication conditions which relate to aplurality of parameters; forming a plurality of conditional segmentsbased on respective values of said plurality of parameters defined insaid plurality of predetermined indication conditions, wherein eachconditional segment of said plurality of conditional segmentscorresponds to a combination of different value ranges of respectiveparameters of said plurality of parameters, obtaining a distributioninformation of said plurality of patient data in said plurality ofconditional segments formed directed to said plurality of predeterminedindication conditions; and determining a matching relation of saidplurality of patient data with an at least one indication condition ofsaid plurality of predetermined indication conditions based on saiddistribution information, wherein said plurality of predeterminedindication conditions comprise a measurement item, a reference standard,a time period, a measurement condition, and a measurement manner.
 8. Thecomputer implemented method according to claim 7, wherein the obtainingsaid distribution information comprises: counting the number of saidplurality of patient data falling into a corresponding conditionalsegment using a plurality of counters set for said plurality ofconditional segments; and obtaining said distribution information ofsaid plurality of patient data in said corresponding conditional segmentby reading a counting of said plurality of counters set for saidplurality of conditional segments.
 9. The computer implemented methodaccording to claim 7, further comprising: determining a required patientdata based on said plurality of indication conditions, wherein theobtaining said distribution information comprises determiningdistribution of said required patient data in said plurality ofconditional segments.
 10. The computer implemented method according toclaim 9, wherein the determining said required patient data comprises:determining said required patient data based on a union of value rangesof an at least one parameter in said plurality of parameters defined insaid plurality of indication conditions.
 11. The computer implementedmethod according to claim 10, wherein the determining said requiredpatient data further comprises: determining a first data portion basedon said union of value ranges of a first parameter in said plurality ofparameters in said plurality of indication conditions; determining asecond data portion based on said union of value ranges of a secondparameter in said plurality of parameters in said plurality ofindication conditions; and determining an intersection of said firstdata portion and said second data portion as said required patient data.12. The computer implemented method according to claim 9, wherein thedetermining said required patient data comprises: determining aplurality of data portions required by a respective indication conditionbased on a plurality of definitions of respective indication conditions;and obtaining a union of said data portions required by said respectiveindication conditions as said required patient data.
 13. The computerimplemented method according to claim 7, wherein the determining saidmatching relation of said plurality of patient data with said at leastone indication condition of said plurality of indication conditionsbased on said distribution information comprises: converting said atleast one indication condition into an operation of data distribution ofan at least one condition segment; and determining said matchingrelation of said plurality of patient data with said at least oneindication condition based on said data distribution of said at leastone conditional segment.
 14. An apparatus for processing patient data,said apparatus comprising: an indication condition obtaining unitconfigured to obtain a plurality of predetermined indication conditionswhich relate to a plurality of parameters; a conditional segment formingunit configured to form a plurality of conditional segments based onrespective values of said plurality of parameters defined in saidplurality of predetermined indication conditions, wherein eachconditional segment of said plurality of conditional segmentscorresponds to a combination of different value ranges of respectiveparameters of said plurality of parameters; a distribution obtainingunit configured to obtain a distribution information of the patient datain said plurality of conditional segments formed directed to saidplurality of predetermined indication conditions; and a matchingdetermining unit configured to determine a matching relation of saidplurality of patient data with an at least one indication condition ofsaid plurality of predetermined indication conditions based on saiddistribution information, wherein said plurality of predeterminedindication conditions comprise a measurement item, a reference standard,a time period, a measurement condition, and a measurement manner. 15.The apparatus according to claim 14, wherein said distribution obtainingunit is configured to: count the numbers of said plurality of patientdata falling into a corresponding conditional segment using a pluralityof counters set for said plurality of conditional segments; and obtainsaid distribution information of said plurality of patient data in saidcorresponding conditional segment by reading a counting of saidplurality of counters set for said plurality of conditional segments.16. The apparatus according to claim 14, further comprising a datadetermining unit configured to: determine a required patient data basedon said plurality of indication conditions, wherein the distributionobtaining unit is configured to determine distribution of said requiredpatient data in said plurality of conditional segments.
 17. Theapparatus according to claim 16, wherein the data determining unit isconfigured to determine said required patient data based on a union ofvalue ranges of an at least one parameter in said plurality ofparameters defined in said plurality of indication conditions.
 18. Theapparatus according to claim 17, wherein said data determining unit isconfigured to: determine a first data portion based on said union ofvalue ranges of a first parameter in said plurality of parameters insaid plurality of indication conditions; determine a second data portionbased on said union of value ranges of a second parameter in saidplurality of parameters in said plurality of indication conditions; anddetermine an intersection of said first data portion and said seconddata portion as said required patient data.
 19. The apparatus accordingto claim 16, wherein said data determining unit is configured to:determine a plurality of data portions required by a respectiveindication conditions based on a plurality of definitions of respectiveindication conditions; and obtain a union of said data portions requiredby said respective indication conditions as said required patient data.20. The apparatus according to claim 14, wherein said matchingdetermining unit is configured to: convert said at least one indicationcondition into an operation of data distribution of an at least onecondition segment; and determine said matching relation of saidplurality of patient data with said at least one indication conditionbased on said data distribution of said at least one conditionalsegment.